Concepts for Improving Machine Learning Based Landslide Assessment

  • Miloš MarjanovićEmail author
  • Mileva Samardžić-Petrović
  • Biljana Abolmasov
  • Uroš Đurić
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)


The main idea of this chapter is to address some of the key issues that were recognized in Machine Learning (ML) based Landslide Assessment Modeling (LAM). Through the experience of the authors, elaborated in several case studies, including the City of Belgrade in Serbia, the City of Tuzla in Bosnia and Herzegovina, Ljubovija Municipality in Serbia, and Halenkovice area in Czech Republic, eight key issues were identified, and appropriate options, solutions, and some new concepts for overcoming them were introduced. The following issues were addressed: Landslide inventory enhancements (overcoming small number of landslide instances), Choice of attributes (which attributes are appropriate and pros and cons on attribute selection/extraction), Classification versus regression (which type of task is more appropriate in particular cases), Choice of ML technique (discussion of most popular ML techniques), Sampling strategy (overcoming the overfit by choosing training instances wisely), Cross-scaling (a new concept for improving the algorithm’s learning capacity), Quasi-hazard concept (introducing artificial temporal base for upgrading from susceptibility to hazard assessment), and Objective model evaluation (the best practice for validating resulting models against the existing inventory). All of them are followed by appropriate practical examples from one of abovementioned case studies. The ultimate objective is to provide guidance and inspire LAM community for a more innovative approach in modeling.


Landslide inventory Susceptibility Hazard Machine learning Sampling Validation Cross-scaling 



This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No TR 36009.


  1. Aronoff S (2005) Remote sensing for GIS managers. ESRI Press, ReadlandsGoogle Scholar
  2. Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Sci 75(19):1318CrossRefGoogle Scholar
  3. Bíl M, Müller I (2008) The origin of shallow landslides in Moravia (Czech Republic) in the spring of 2006. Geomorphology 99:246–253CrossRefGoogle Scholar
  4. Božović B, Lazić M, Sunarić D, Todorović B (1981) Prikaz stepena istraženosti i kritička analiza metodologije dosadašnjih istraživanja stabilnosti terena beogradskog područja, Simpozijum Istraživanje i sanacija klizišta, Bled, Slovenija, Knjiga 1. Zbornik radova, pp 107–118 (in Serbian)Google Scholar
  5. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159CrossRefGoogle Scholar
  6. Brennan RL, Prediger DJ (1981) Coefficient kappa: some uses, misuses, and alternatives. Educ Psychol Measur 41(3):687–699CrossRefGoogle Scholar
  7. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378CrossRefGoogle Scholar
  8. Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75:238CrossRefGoogle Scholar
  9. Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. ICML ‘06 Proceedings of the 23rd international conference on machine learning, Pittsburgh, June 2006, pp 161–168Google Scholar
  10. Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327CrossRefGoogle Scholar
  11. Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23CrossRefGoogle Scholar
  12. Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472CrossRefGoogle Scholar
  13. Ciurleo M, Cascini L, Calvello M (2017) A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils. Eng Geol 223:71–81CrossRefGoogle Scholar
  14. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46CrossRefGoogle Scholar
  15. Conoscenti C, Rotigliano E, Cama M, Caraballo-Arias NA, Lombardo L, Agnesi V (2016) Exploring the effect of absence selection on landslide susceptibility models: a case study in Sicily, Italy. Geomorphology 261:222–235CrossRefGoogle Scholar
  16. Dumlao AJ, Victor JA (2015) GIS-aided statistical landslide susceptibility modeling and mapping of Antipolo Rizal (Philippines). IOP Conf Ser: Earth Environ Sci 26:12031. Scholar
  17. Đurić D, Mladenović A, Pešić-Georgiadis M, Marjanović M, Abolmasov B (2017) Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia. Landslides, Scholar
  18. Erener A, Mutlub A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55CrossRefGoogle Scholar
  19. Feinstein AR, Cicchetti DV (1990) High agreement but low kappa: I. the problems of two paradoxes. J Clin Epidemiol 43(6):543–549CrossRefGoogle Scholar
  20. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201CrossRefGoogle Scholar
  21. Foumelis M, Lekkas E, Parcharidis I (2004) Landslide susceptibility mapping by GIS-based qualitative weighting procedure in Corinth Area, Bulletin of the Geological Society of Greece, vol XXXVI, 2004 Proceedings of the 10th International Congress, Thessaloniki, April 2004, pp 904–912Google Scholar
  22. Gallus D, Abecker A (2008) Classification of landslide susceptibility in the development of early warning systems. 11th AGILE International Conference on Geographic Information Science, University of Girona, Spain, pp 1–17Google Scholar
  23. Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. Scholar
  24. Gojgić D, Petrović N, Komad Z (1995) Katastar klizišta i nestabilnih padina u funkciji prostornog i urbanističkog planiranja, projektovanja i građenja, II Simpozijum Istraživanje i sanacija klizišta, D. Milanovac, Srbija, pp 103–111 (in Serbian)Google Scholar
  25. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth-Sci Rev 112:42–66CrossRefGoogle Scholar
  26. Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6):1437–1447CrossRefGoogle Scholar
  27. Hastie T, Tibshirani RI, Frieman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkCrossRefGoogle Scholar
  28. Heymann Y, Steenmans C, Croissille G, Bossard M (1994) CORINE Land Cover. Technical Guide, Official Publications of the European CommunitiesGoogle Scholar
  29. Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198–208CrossRefGoogle Scholar
  30. Jones R (2002) Algorithms for using a DEM for mapping catchment areas of stream sediment samples. Comput Geosci 28:1051–1060. Scholar
  31. Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439. Scholar
  32. Kircher K, Krejčí O, Máčka Z, Bíl M (2000) Slope deformations in Eastern Moravia, Vsetín District (Outer Western Carpathians). Acta Universitas Carolinae 35:133–143Google Scholar
  33. Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. CATENA 152:144–162CrossRefGoogle Scholar
  34. Kukemilks K, Saks T (2013) Landslides and gully slope erosion on the banks of the Gauja River between the towns of Sigulda and Līgatne. Est J Earth Sci 62:231. Scholar
  35. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 3:159–174CrossRefGoogle Scholar
  36. Lokin P, Pavlović R, Trivić B, Lazić M, Batalović K, Đurić U (2012) Belgrade landslide cadastre, XIV simpozijum iz Inženjerske geologije i Geotehnike, proceedings, Belgrade, Serbia, pp 389–403 (in Serbian)Google Scholar
  37. Lombardo L, Cama M, Maerker M, Rotigliano E (2014) A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster. Nat Hazards 74:1951–1989CrossRefGoogle Scholar
  38. Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci (Calcutta) 2:49–55Google Scholar
  39. Malamud B, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29:687–711CrossRefGoogle Scholar
  40. Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234CrossRefGoogle Scholar
  41. Marjanović M (2013) Comparing the performance of different landslide susceptibility models in ROC space. In: Margottini C et al (eds) Landslide science and practice, vol 1, Springer, Berlin. Scholar
  42. Marjanović M (2014) Conventional and machine learning methods for landslide assessment in GIS. Palacky University, Olomouc, Czech RepublicGoogle Scholar
  43. Marjanović M, Đurić U, (2016) From landslide inventory to landslide risk assessment: methodology, current practice and challenges. III Congress of Geologists of the Republic of Macedonia, 30 Sept–2 Oct 2016, Struga Macedonia, pp 199–208Google Scholar
  44. Mitchell TM (1997) Machine learning. McGraw Hill, New YorkGoogle Scholar
  45. Moosavi V, Niazi Y (2016) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114CrossRefGoogle Scholar
  46. Mumic E, Glade T, Hasel S (2013) Analysis of landslides triggered in 2010 in Tuzla, Bosnia and Herzegowina. Geophysical Research Abstracts EGU General Assembly 2013, Abstract #13016Google Scholar
  47. Oommen T, Baise LG, Vogel RM (2011) Sampling bias and class imbalance in maximum-likelihood logistic regression. Math Geosci 43:99–120CrossRefGoogle Scholar
  48. Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443. Scholar
  49. Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250CrossRefGoogle Scholar
  50. Pontius RG Jr, Millones M (2011) Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32(15):4407–4429CrossRefGoogle Scholar
  51. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. Scholar
  52. Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70. Scholar
  53. Sharma LP, Patel N, Debnath P, Ghose MK (2012) Assessing landslide vulnerability from soil characteristics-a GIS-based analysis. Arab J Geosci 5:789–796. Scholar
  54. Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76(2):60CrossRefGoogle Scholar
  55. Spitznagel EL, Helzer JE (1985) A proposed solution to the base rate problem in the kappa statistic. Arch Gen Psychiatry 42(7):725–728CrossRefGoogle Scholar
  56. Steger S, Brenning A, Bell R, Petschko H, Glade T (2016) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23CrossRefGoogle Scholar
  57. Tsangaratos P, Ilia I (2017) Landslide assessments through soft computing techniques within a GIS-based framework. Am J Geogr Inf Syst 6(1A),
  58. Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through an artificial neural network classifier. Nat Hazards 74(3):1489–1516CrossRefGoogle Scholar
  59. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. International Association for Engineering Geology, Paris, France, p 63Google Scholar
  60. Wang Q, Li W, Chen W, Bai H (2015) GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J Earth Syst Sci 124(7):1399–1415CrossRefGoogle Scholar
  61. Witten IH, Frank E, Hall MA (2011) Data mining practical machine learning tools and techniques. Elsevier, BurlingtonGoogle Scholar
  62. Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287CrossRefGoogle Scholar
  63. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582. Scholar
  64. Yilmaz I (2009) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836. Scholar
  65. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13:839–856CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Miloš Marjanović
    • 1
    Email author
  • Mileva Samardžić-Petrović
    • 2
  • Biljana Abolmasov
    • 1
  • Uroš Đurić
    • 2
  1. 1.Faculty of Mining and GeologyUniversity of BelgradeBelgradeSerbia
  2. 2.Faculty of Civil EngineeringUniversity of BelgradeBelgradeSerbia

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